In many situations, making additional modeling assumptions can be sufficient and valid to establish some form of identifiability, depending on the application. Human experts can assess the applicability of such assumptions to the domain, as well as provide any additional constraints. For example, assumptions such as consistency, monotonicity [1]}, or generalizations thereof are often used in epidemiological literature to estimate counterfactuals from observational data and have recently been explored in the context of OPE for model debugging [2]}. In Example 2, assuming treatments are binary is a form of simple parametric assumption that can reduce the aleatoric uncertainty by reducing the number of interventional experiments that need to be conducted. These assumptions aid identifiability by making fundamental assumptions about the nature of counterfactuals, reducing aleatoric uncertainty. Additional knowledge like linearity and other functional assumptions, like the availability of noisy proxies of confounders [3]}, [4]}, or availability of instrumental variables can help constrain the amount of online experimentation required for OPE [5]}. These assumptions can help reduce modeling uncertainty, during estimation as well as reduce aleatoric uncertainty by constraining counterfactual distributions to specific functional forms.
